The present invention relates of a preference learning apparatus, preference learning system, preference learning method, and recording medium for collecting and learning preference information considering time and place from a portable information terminal that a user is carrying.
Conventionally, examinations have been done about an information filtering technique for selecting information corresponding to the user""s preference from an enormous quantity of digitized information (contents) published on the Internet or an information navigation technique using the information filtering. In the information filtering technique, to quantify the degree of user""s interest and process it by a computer, a vector expressing what kind of content is a user""s interest is often used.
In such a technique, the user""s preference must be properly learned. To learn a user""s preference, a user""s preference is estimated from the user""s log of access to contents. One of such methods is disclosed in Japanese Patent Laid-Open No. 11-15840 in which information following the preference of a user, that changes along with the elapse of time, is automatically selected without requiring any explicit setting and evaluation from the user himself/herself. Another method has also been proposed in which the movement pattern of a user who uses a portable information terminal is monitored, and the user""s preference information is automatically learned on the basis of information related to a place where the user stayed.
In these conventional preference learning methods, however, user""s preference information is learned without taking any time zone and place into consideration. For this reason, under circumstances unique to a mobile environment where the preference changes in accordance with the user""s situation, a content that is optimum for the time zone and place where the user is present cannot be provided in response to a browsing request from the user. In the mobile environment, when a user searches for POI (Point Of Interest) information such as restaurant information or sightseeing spot information using a portable information terminal or a car navigation system with an information search function, the user""s preference information changes depending on the time zone and place.
For example, a user who often uses a fast-food restaurant at lunch time may not go to a fast-food restaurant but to an exclusive restaurant at suppertime. A user who often goes to an Italian restaurant in Tokyo may rather be fond of a local meal at a tour.
It is an object of the present invention to provide a preference learning apparatus, preference learning system, preference learning method, and recording medium which can learn preference information of a user who uses a portable information terminal in accordance with the time zone and place in which the user behaves.
It is another object of the present invention to provide a preference learning apparatus, preference learning system, preference learning method, and recording medium which can extract and manage user""s preference information that changes.
In order to achieve the above objects, according to the present invention, there is provided a preference learning apparatus for detecting a user""s action from a portable information terminal to which various kinds of contents are provided through a communication channel and learning a user""s preference on the basis of a detected action log, comprising a content attribute information database for storing, for each content, an attribute as an object of the learning contained in each of various kinds of contents, and an attribute value, a action information database for storing, for each action, an attribute as an object of the learning estimated from the user""s action and a weight for the attribute, a time information correlation table for storing a name and time range of a time zone in correspondence with each other, an area information correlation table for storing each area name and area range in correspondence with each other for each of a plurality of areas which classify position information of the user, a user""s preference information database for storing, for each user""s preference information containing an attribute/attribute value as objects of the learning, a weight for the attribute, a time zone when the weight is valid, and a place where the weight is valid, user action detection means for detecting the user""s action on the basis of information obtained from the portable information terminal and acquiring detection data containing a user ID indicating the user, a action name indicating the action, a content ID indicating a content related to an object of the action, and a measurement time and position information at which the action has been detected, and preference information management means for updating the user""s preference information database on the basis of preference analysis data obtained by analyzing the user""s preference on the basis of the detection data output from the user action detection means, wherein the preference information management means generates the preference analysis data using time zone information acquired from the time information correlation table on the basis of the measurement time contained in the detection data output from the user action detection means, the area name acquired from the area information correlation table on the basis of the position information contained in the detection data, the attribute and weight contained in the action which are acquired from the action information database on the basis of the action name contained in the detection data, and the attribute value acquired from the content attribute information database on the basis of the attribute related to the preference and the content ID contained in the detection data, and updates, with the weight contained in the generated preference analysis data, the weight contained in the preference information in the user""s preference information database, which is specified by the time zone information, area name, and attribute/attribute value as objects of the learning, which are contained in the generated preference analysis data, and the user ID contained in the detection data.